Hyperparameter Tuning
Hyperparameter tuning is the process of searching for the best set of hyperparameter values to maximize a model’s performance. Unlike parameters (weights and biases) learned during training, hyperparameters must be predefined and carefully optimized.
Background
The performance of machine learning models often depends more on hyperparameters than on the choice of algorithm itself. Common techniques include:
- Grid Search: exhaustive exploration of predefined combinations.
- Random Search: sampling combinations randomly, often more efficient.
- Bayesian Optimization: modeling the search space to focus on promising regions.
Examples
- Neural networks: tuning learning rate, dropout, and batch size.
- Decision trees and random forests: adjusting depth and number of estimators.
- Support Vector Machines (SVMs): selecting optimal kernel and penalty parameters.
Strengths and challenges
- ✅ Leads to models with higher accuracy and better generalization.
- ✅ Automation tools (e.g., Optuna, Hyperopt, Ray Tune) streamline the process.
- ❌ Computationally expensive, especially with deep learning.
- ❌ Overfitting risk if tuning is performed improperly.
Hyperparameter tuning is often seen as both an art and a science. A poorly tuned model can underperform even if the underlying algorithm is state-of-the-art, while a well-tuned model can extract surprising performance from simple methods. For this reason, practitioners often spend as much time on tuning as on designing architectures.
Beyond classical grid and random search, advanced approaches are now common:
- Bayesian optimization uses past evaluations to predict which hyperparameter sets are promising.
- Evolutionary algorithms explore the search space by mimicking natural selection.
- Hyperband and successive halving allocate resources dynamically, discarding bad configurations early.
A modern trend is AutoML frameworks that automate much of the search, making hyperparameter tuning more accessible. Still, experts caution that blind automation may miss domain-specific insights. Tuning should be guided not only by metrics but also by considerations like training cost, fairness, and robustness.
📚 Further Reading
- Bergstra, J., Bengio, Y. (2012). Random Search for Hyper-Parameter Optimization.
